Python - Bedrock: Claude 3 Demo (awsdocs#6295)

python/example_code/bedrock-runtime/models/anthropic/claude3_chatbot_demo/0_get_models.py

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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+import os
+
+import boto3
+
+from utils.custom_logging import setup_custom_logger
+from utils.timeit import timeit
+
+
+class BedrockModelService:
+    """
+    A class to interact with the Amazon Bedrock service, specifically for operations
+    related to foundation models.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BedrockModelService with a boto3 client for Bedrock and a custom logger.
+        """
+        self.bedrock_client = boto3.client(service_name="bedrock")
+        self.logger = setup_custom_logger(os.path.basename(__file__))
+
+    @timeit
+    def get_models(self):
+        """
+        Fetches a list of foundation models from the Bedrock AI service and logs their IDs.
+
+        This method uses the `boto3` library to interact with the Bedrock AI service,
+        retrieving a list of foundation models. Each model's ID is then logged using
+        a custom logger. The method is decorated with `@timeit` to measure its execution time.
+
+        Exception Handling:
+            Catches and logs exceptions that may occur during the interaction with the Bedrock service.
+
+        Logging:
+            Logs the total number of models found and each model's ID at DEBUG level. If no models are found
+            or an exception occurs, appropriate warnings or errors are logged.
+        """
+        try:
+            # Request a list of foundation models from Bedrock
+            model_list = self.bedrock_client.list_foundation_models()
+
+            # Extract model summaries from the response
+            model_summaries = model_list.get("modelSummaries")
+            if model_summaries is not None:
+                self.logger.info(f"Found models: {len(model_summaries)}")
+                # Log each model's ID
+                for model in model_summaries:
+                    self.logger.debug(model["modelId"])
+            else:
+                self.logger.warning("No model summaries found in the Bedrock response.")
+        except Exception as e:
+            # Log any exceptions that occur during the process
+            self.logger.error(
+                f"Failed to retrieve models from Bedrock: {e}", exc_info=True
+            )
+
+
+bedrock_service = BedrockModelService()
+bedrock_service.get_models()

python/example_code/bedrock-runtime/models/anthropic/claude3_chatbot_demo/0_try_claude3.py

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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+import os
+
+import boto3
+
+from utils.custom_logging import setup_custom_logger
+from utils.timeit import timeit
+
+
+class BedrockAIConverter:
+    """
+    A class that provides methods for converting between images and text, and generating images from text,
+    using generative AI models hosted on Amazon Bedrock.
+    """
+
+    def __init__(self):
+        """
+        Initializes the BedrockAIConverter with a BedrockRuntime client and a custom logger.
+        """
+        self.bedrock_runtime_client = boto3.client("bedrock-runtime")
+        self.logger = setup_custom_logger(os.path.basename(__file__))
+
+    @timeit
+    def img_to_txt(self):
+        """
+        Converts an image to text by sending the image to a generative AI model
+        hosted on Bedrock. Reads an image file, encodes it in base64, and sends
+        it to the model with a prompt. Logs and returns the model's response.
+
+        Returns:
+            str: The text generated by the model based on the image.
+
+        Raises:
+            Exception: If the process fails at any point.
+        """
+        try:
+            import base64
+            import json
+            import sys
+
+            sys.path.append("../data/resources")
+            with open("../data/resources/entry.jpeg", "rb") as image_file:
+                encoded_string = base64.b64encode(image_file.read())
+                base64_string = encoded_string.decode("utf-8")
+
+            prompt = """
+                ######
+                Describe the site in the photo with as much detail as you can.
+                Do not mention an image, as it will confuse later prompting in this chain.
+                Just provide a description of the site with no mention of any image.
+                ######
+            """
+            self.logger.info(f"PROMPT:\n{prompt}\n")
+
+            payload = {
+                "modelId": "anthropic.claude-3-sonnet-20240229-v1:0",
+                "contentType": "application/json",
+                "accept": "application/json",
+                "body": {
+                    "anthropic_version": "bedrock-2023-05-31",
+                    "max_tokens": 1000,
+                    "messages": [
+                        {
+                            "role": "user",
+                            "content": [
+                                {
+                                    "type": "image",
+                                    "source": {
+                                        "type": "base64",
+                                        "media_type": "image/png",
+                                        "data": base64_string,
+                                    },
+                                },
+                                {
+                                    "type": "text",
+                                    "text": prompt,
+                                },
+                            ],
+                        }
+                    ],
+                },
+            }
+
+            body_bytes = json.dumps(payload["body"]).encode("utf-8")
+            response = self.bedrock_runtime_client.invoke_model(
+                body=body_bytes,
+                contentType=payload["contentType"],
+                accept=payload["accept"],
+                modelId=payload["modelId"],
+            )
+
+            response_body = response["body"].read().decode("utf-8")
+            data = json.loads(response_body)
+            text = data["content"][0]["text"]
+            self.logger.warning(text)
+            return text
+        except Exception as e:
+            self.logger.error(f"Failed to convert image to text: {e}")
+            raise
+
+    @timeit
+    def txt_to_txt(self, text):
+        """
+        Invokes a text-to-text generative AI model to generate an idea based on the given text description.
+        Logs and returns the model's generated text.
+
+        Args:
+            text (str): The input text description to base the generative model's response on.
+
+        Returns:
+            str: The text generated by the model.
+
+        Raises:
+            Exception: If the process fails at any point.
+        """
+        try:
+            import json
+
+            prompt = f"""
+                ######
+                You are a skilled event planner with a knack for coming
+                up with low-cost high tech improvements to event spaces,
+                particularly for tech conferences.
+
+                Based on the following description, describe a single thrilling
+                design improvement that would heighten the experience at the
+                rvatech/ AI & Data Summit 2024, which is a day-long event
+                where attendees will pass through this site in high
+                volumes. Something small, nerdy, tech-oriented, and interactive.
+
+                Update the starter description provided in such a way that
+                your new improvement idea has been implemented. How would it look in the 
+                site description provided?
+
+                Starter Description: {text}
+
+                Updated description:
+                ######
+            """
+            self.logger.info(f"PROMPT:\n{prompt}\n")
+
+            response = self.bedrock_runtime_client.invoke_model(
+                modelId="anthropic.claude-3-sonnet-20240229-v1:0",
+                body=json.dumps(
+                    {
+                        "anthropic_version": "bedrock-2023-05-31",
+                        "max_tokens": 1024,
+                        "messages": [
+                            {
+                                "role": "user",
+                                "content": [{"type": "text", "text": prompt}],
+                            }
+                        ],
+                    }
+                ),
+            )
+
+            result = json.loads(response.get("body").read())
+            self.logger.critical(response)
+            output_list = result.get("content", [])
+            text = output_list[0]["text"]
+            self.logger.warning(text)
+            return text
+        except Exception as e:
+            self.logger.error(f"Failed to generate text from text: {e}")
+            raise
+
+    @timeit
+    def txt_to_img(self, text):
+        """
+        Converts given text to an image by sending the text to a generative AI model
+        that produces images. Decodes and saves the response image.
+
+        Args:
+            text (str): The text description to convert to an image.
+
+        Raises:
+            Exception: If the process fails at any point.
+        """
+        try:
+            import json
+            from base64 import b64decode
+            from io import BytesIO
+
+            from PIL import Image
+
+            body = json.dumps(
+                {
+                    "text_prompts": [{"text": text}],
+                    "cfg_scale": 6,
+                    "seed": 10,
+                    "steps": 50,
+                }
+            )
+            self.logger.info("PROMPT:\n" + f"\n{text}\n")
+            modelId = "stability.stable-diffusion-xl"
+            accept = "application/json"
+            contentType = "application/json"
+            response = self.bedrock_runtime_client.invoke_model(
+                body=body, modelId=modelId, accept=accept, contentType=contentType
+            )
+            response = json.loads(response.get("body").read())
+            images = response.get("artifacts")
+            image = Image.open(BytesIO(b64decode(images[0].get("base64"))))
+            image.save("2_try_claude3.png")
+        except Exception as e:
+            self.logger.error(f"Failed to convert text to image: {e}")
+            raise
+
+
+converter = BedrockAIConverter()
+description = converter.img_to_txt()
+design_idea = converter.txt_to_txt(description)
+converter.txt_to_img(
+    f"""
+    #####
+    You are a whimsical artist with big ideas.
+
+    Generate a new detailed drawing showcasing the following site:
+    {design_idea}
+    ######
+    """
+)

python/example_code/bedrock-runtime/models/anthropic/claude3_chatbot_demo/README.md

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+# Running a RAG-enhanced chatbot on Bedrock using Claude 3, Knowledge Base, and LangChain
+This directory contains code used for a live demo. It should be run sequentially, as follows:
+1. 0_get_models.py
+2. 0_try_claude3.py
+3. fordgpt_v1.py
+4. fordgpt_v2.py
+5. fordgpt_v3.py
+
+Note: FordGPTv2-3 (step 4-5) require AWS data stores containing certain information that is not included in this repository.
+
+### Usage
+This is reference code that should not be copied directly into production without review.
+
+Running foundation models costs money. This demo presumes you have billing alarms configured and an understanding of Amazon Bedrock pricing.
+
+## About FordGPT
+![fordgpt.jpg](fordgpt.jpg)
+
+# Requirements
+Speaks fluently on:
+* Popular companies
+* Meetup groups & events
+* Best coffee shops for coding
+* Fail-safe job hunting resources